MR Brain Tissue Classification Using an Edge-Preserving Spatially Variant Bayesian Mixture Model
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Generalized competitive learning of Gaussian mixture models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
A Rician mixture model classification algorithm for magnetic resonance images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Spatially Varying Mixtures Incorporating Line Processes for Image Segmentation
Journal of Mathematical Imaging and Vision
A fast and robust image segmentation using FCM with spatial information
Digital Signal Processing
Joint recovery and segmentation of polarimetric images using a compound MRF and mixture modeling
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
A color- and texture-based image segmentation algorithm
Machine Graphics & Vision International Journal
Image segmentation via coherent clustering in L*a*b* color space
Pattern Recognition Letters
Segmentation of brain images using adaptive atlases with application to ventriculomegaly
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Dirichlet Gaussian mixture model: Application to image segmentation
Image and Vision Computing
A spatially-constrained normalized Gamma process prior
Expert Systems with Applications: An International Journal
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A finite mixture model for detail-preserving image segmentation
Signal Processing
Automatic image segmentation using constraint learning and propagation
Digital Signal Processing
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We propose a new approach for image segmentation based on a hierarchical and spatially variant mixture model. According to this model, the pixel labels are random variables and a smoothness prior is imposed on them. The main novelty of this work is a new family of smoothness priors for the label probabilities in spatially variant mixture models. These Gauss-Markov random field-based priors allow all their parameters to be estimated in closed form via the maximum a posteriori (MAP) estimation using the expectation-maximization methodology. Thus, it is possible to introduce priors with multiple parameters that adapt to different aspects of the data. Numerical experiments are presented where the proposed MAP algorithms were tested in various image segmentation scenarios. These experiments demonstrate that the proposed segmentation scheme compares favorably to both standard and previous spatially constrained mixture model-based segmentation